Demystifying the Technical Structure of Text-to-Speech Models
In recent years, text-to-speech (TTS) models have made remarkable strides in generating natural and human-like speech. These models have found applications in various fields, including virtual assistants, audiobook production, and accessibility solutions. Behind the scenes, TTS models employ intricate architectures and advanced techniques to convert written text into intelligible spoken words. In this blog post, we will explore the technical structure of text-to-speech models and gain insight into how they work.
Sequence-to-Sequence Models:
Text-to-speech models are often based on the sequence-to-sequence (seq2seq) architecture, which is a popular framework for many natural language processing tasks. Seq2seq models consist of an encoder and a decoder. The encoder processes the input text and extracts its contextual information, while the decoder generates the corresponding speech waveform.
Text Encoding:
To convert textual input into meaningful representations, TTS models employ various text encoding techniques. One common approach is to use recurrent neural networks (RNNs), such as long short-term memory (LSTM) or gated recurrent units (GRUs), to process the input text sequentially and capture its linguistic features. Alternatively…